Regularisation based approaches apply a calibrating penalty to the supervised learning objective. [1]} added negative entropy of the predictions to encourage the model to predict less `peaky' estimates. Subsequently, many approaches have been proposed along this direction which adds noise to the labels [2]}, optimise a proxy for the calibration error metric [3]}, and replace the cross-entropy objective with focal loss [4]}. [5]} utilised human inferred soft-targets to improve robustness. This approach can be understood as being along the lines of label smoothing.
